Radio Messaging Frequency, Information Framing, and Consumer Willingness to Pay for Biofortified Iron Beans: Evidence from Revealed Preference Elicitation in Rural Rwanda
Bibliographic record
Abstract
Iron deficiency is a public health problem in many developing countries. Iron‐biofortified varieties of commonly consumed staple crops have the potential to contribute to the daily iron requirements in diets. This paper examines consumer acceptance and willingness to pay (WTP) for two iron bean varieties in Rwanda: red iron bean (RIB) and white iron bean (WIB). Using the Becker‐DeGroot‐Marschak mechanism, the paper investigates the effect of (1) nutrition information; (2) information frame; and (3) the frequency of providing the information on consumer WTP. WTP estimations take into account social interaction and nonpayment effects. Results indicate that without information about the nutritional benefits of the two iron bean varieties, consumers are willing to pay a large premium for the RIB variety, but not for the WIB variety. The nutrition information provided has a significantly positive effect on the premium for each of the iron bean varieties. Results also indicate that the effects of how the information is framed on this premium are not statistically significant. However, providing the nutrition information three times versus once significantly increases consumer demand for the WIB variety. These findings could inform the design of efficient delivery and marketing strategies for iron bean varieties in Rwanda.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".